The Keio Journal of Medicine
Online ISSN : 1880-1293
Print ISSN : 0022-9717
ISSN-L : 0022-9717
ORIGINAL ARTICLES
Diagnostic Performance of Computed Tomography Imaging for COVID-19 in a Region with Low Disease Prevalence
Ho LeeTatsuya SuzukiYohei OkadaHiromu TanakaSatoshi OkamoriHirofumi KamataMakoto IshiiMasahiro JinzakiKoichi Fukunaga
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2022 Volume 71 Issue 1 Pages 21-30

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Abstract

Coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China, in December 2019 as an outbreak of pneumonia of unknown origin. Previous studies have suggested the utility of chest computed tomography (CT) in the diagnosis of COVID-19 because of its high sensitivity (93%–97%), relatively simple procedure, and rapid test results. This study, performed in Japan early in the epidemic when COVID-19 prevalence was low, evaluated the diagnostic accuracy of chest CT in a population presenting with lung diseases having CT findings similar to those of COVID-19. We retrospectively included all consecutive patients (≥18 years old) presenting to the outpatient department of Keio University Hospital between March 1 and May 31, 2020, with fever and respiratory symptoms. We evaluated the performance of diagnostic CT for COVID-19 by using polymerase chain reaction (PCR) results as the reference standard. We determined the numbers of false-positive (FP) results and assessed the clinical utility using decision curve analysis. Of the 175 patients, 22 were PCR-positive. CT had a sensitivity of 68% and a specificity of 57%. Patients with FP results on CT diagnosis were mainly diagnosed with diseases mimicking COVID-19, e.g., interstitial lung disease. Decision curve analysis indicated that the clinical utility of CT imaging was limited. The diagnostic performance of CT for COVID-19 was inadequate in an area with low COVID-19 prevalence and a high prevalence of other lung diseases with chest CT findings similar to those of COVID-19. Considering this insufficient diagnostic performance, CT findings should be evaluated in the context of additional medical information to diagnose COVID-19.

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© 2022 by The Keio Journal of Medicine
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